Estimation of the spatial weights matrix under structural constraints
AbstractWhile estimates of models with spatial interaction are very sensitive to the choice of spatial weights, considerable uncertainty surrounds the definition of spatial weights in most studies with cross-section dependence. We show that, in the spatial error model, the spatial weights matrix is only partially identified, and is fully identified under the structural constraint of symmetry. For the spatial error model, we propose a new methodology for estimation of spatial weights under the assumption of symmetric spatial weights, with extensions to other important spatial models. The methodology is applied to regional housing markets in the UK, providing an estimated spatial weights matrix that generates several new hypotheses about the economic and socio-cultural drivers of spatial diffusion in housing demand.
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Bibliographic InfoArticle provided by Elsevier in its journal Regional Science and Urban Economics.
Volume (Year): 43 (2013)
Issue (Month): 4 ()
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Web page: http://www.elsevier.com/locate/regec
Spatial econometrics; Spatial autocorrelation; Spatial weights matrix; Spatial error model; Partial identification; Housing demand; Gradient projection;
Find related papers by JEL classification:
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
- C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
- R21 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Housing Demand
- R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Production Analysis, and Firm Location - - - Housing Supply and Markets
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- Abhimanyu Gupta & M. Robinson, 2013. "Inference on Higher-Order Spatial Autoregressive Models with Increasingly Many Parameters," Economics Discussion Papers 735, University of Essex, Department of Economics.
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